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// Copyright 2014 BVLC and contributors.
//
#include <algorithm>
#include <vector>
#include "caffe/layer.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
Layer<Dtype>::SetUp(bottom, top);
(*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
bottom[0]->height(), bottom[0]->width());
sum_multiplier_.Reshape(1, bottom[0]->channels(),
bottom[0]->height(), bottom[0]->width());
Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
for (int i = 0; i < sum_multiplier_.count(); ++i) {
multiplier_data[i] = 1.;
}
scale_.Reshape(bottom[0]->num(), 1, 1, 1);
}
template <typename Dtype>
Dtype SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = (*top)[0]->mutable_cpu_data();
Dtype* scale_data = scale_.mutable_cpu_data();
int num = bottom[0]->num();
int dim = bottom[0]->count() / bottom[0]->num();
caffe_copy(bottom[0]->count(), bottom_data, top_data);
// we need to subtract the max to avoid numerical issues, compute the exp,
// and then normalize.
for (int i = 0; i < num; ++i) {
scale_data[i] = bottom_data[i*dim];
for (int j = 0; j < dim; ++j) {
scale_data[i] = std::max(scale_data[i], bottom_data[i * dim + j]);
}
}
// subtraction
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
scale_data, sum_multiplier_.cpu_data(), 1., top_data);
// Perform exponentiation
caffe_exp<Dtype>(num * dim, top_data, top_data);
// sum after exp
caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_data,
sum_multiplier_.cpu_data(), 0., scale_data);
// Do division
for (int i = 0; i < num; ++i) {
caffe_scal<Dtype>(dim, Dtype(1.) / scale_data[i], top_data + i * dim);
}
return Dtype(0);
}
template <typename Dtype>
void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
vector<Blob<Dtype>*>* bottom) {
const Dtype* top_diff = top[0]->cpu_diff();
const Dtype* top_data = top[0]->cpu_data();
Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
Dtype* scale_data = scale_.mutable_cpu_data();
int num = top[0]->num();
int dim = top[0]->count() / top[0]->num();
caffe_copy(top[0]->count(), top_diff, bottom_diff);
// Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
for (int i = 0; i < num; ++i) {
scale_data[i] = caffe_cpu_dot<Dtype>(dim, top_diff + i * dim,
top_data + i * dim);
}
// subtraction
caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
scale_data, sum_multiplier_.cpu_data(), 1., bottom_diff);
// elementwise multiplication
caffe_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
}
#ifdef CPU_ONLY
STUB_GPU(SoftmaxLayer);
#endif
INSTANTIATE_CLASS(SoftmaxLayer);
} // namespace caffe
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